विधियों की तुलना करें

	न्यूरल आर्किटेक्चर सर्च	XGBoost
क्षेत्र	गहन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017	2016
प्रवर्तक	Zoph, B. & Le, Q.V.	Chen, T. & Guestrin, C.
प्रकार	Automated architecture optimization (deep learning)	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
उपनाम	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	5	5
सारांश	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.